Physiology_Pipeline
Kevin Wong
22/07/2021
Overview
This markdown includes data, code, figures, and statstics for the physiology analysis of the Porites Rim Bleaching experiment.
Loading libraries and metadata files
# load packages
library(dplyr)
library(tidyverse)
library(readr)
library(stringr)
library(gridExtra)
library(grid)
library(ggplot2)
library(lattice)
library(Rmisc)
library(ggpubr)
library(lsmeans)
library("reshape")
library("arsenal")
library(RColorBrewer)
library(lme4)
library(lmerTest)
library(car)
library(effects)
library(ggfortify)
library(cowplot)
library(vegan)
library(corrr)
library(ggcorrplot)
library(GGally)
library(broom)
library(cowplot)
library(RVAideMemoire)
library(mixOmics)
library(factoextra)
library("segmented")
library("plotrix")
library("lubridate")
library(lsmeans)
# Load metadata
vial.meta <- read.csv("../data/Physiology/Vial_Metadata.csv")
frag.meta <- read.csv("../data/Physiology/Airbrush_Metadata.csv")
vial.meta$coral.tp <- paste(vial.meta$Fragment.ID, vial.meta$Timepoint, sep = "-")
frag.meta$coral.tp <- paste(frag.meta$Fragment.ID, frag.meta$Timepoint, sep = "-")
master.meta <- merge(vial.meta, frag.meta, by = "coral.tp")Endosymbiont Density
#Import Data
Zoox <- read.csv("../data/Physiology/Symbiont_Counts/Symbiont_Counts.csv")
#Calculating cells/Larvae from cells/count
Zoox$Average <- rowMeans(Zoox[ ,3:8]) #averaging counts
Zoox$Cells.mL <- (Zoox$Average/Zoox$Num.Squares)/0.0001 #number of cells per mL - volume of haemocytometer
# Merging metadata
zoox.meta <- merge(Zoox, master.meta, by = "Vial")
#Accounting for homogenate volume
zoox.meta$Cells.mL.vol <- zoox.meta$Cells.mL * zoox.meta$Homogenate_Vol.mL
#Normalizing to fragment surface area
zoox.meta$Cells.cm2 <- zoox.meta$Cells.mL.vol/zoox.meta$Surface_Area_cm2
zoox.meta$Cells.cm2.x6 <- zoox.meta$Cells.cm2 / 1000000
#Removing metadata columns
zoox.final <- zoox.meta %>%
dplyr::select("Fragment.ID.y","Day", "Group", "Group", "Cells.cm2.x6")
zoox.final$Day <- as.factor(zoox.final$Day)
zoox.final$Group <- as.factor(zoox.final$Group)
names(zoox.final)[1] <- "Fragment.ID"
write.csv(zoox.final, "../output/Physiology/Zoox.Calc.csv")Chlorophyll a and c2
# Import data
chla.raw.0708 <- read.csv("../data/Physiology/Chlorophyll/20210708_Chl.csv")
chla.raw.0716 <- read.csv("../data/Physiology/Chlorophyll/20210716_Chl.csv")
chla.meta <- read.csv("../data/Physiology/Chlorophyll/Chlorophyll_Meta.csv")
# Adding Run number to datasets
chla.raw.0708$Date <- 20210708
chla.raw.0716$Date <- 20210716
# Make a unique column
chla.raw.0708$date.well <- paste(chla.raw.0708$Date, chla.raw.0708$Well, sep = "-")
chla.raw.0716$date.well <- paste(chla.raw.0716$Date, chla.raw.0716$Well, sep = "-")
chla.meta$date.well <- paste(chla.meta$Date, chla.meta$Well, sep = "-")
# Attaching vial metadata
chla.data.0708 <- merge(chla.raw.0708, chla.meta, by = "date.well")
chla.data.0716 <- merge(chla.raw.0716, chla.meta, by = "date.well")
# Blank 750nm correction for each run separately
Blank.0708 <- chla.data.0708 %>%
filter(Sample.Type == "Blank") %>%
summarise(blk.avg = mean(Chl.750))
Blank.0716 <- chla.data.0716 %>%
filter(Sample.Type == "Blank") %>%
summarise(blk.avg = mean(Chl.750))
# Subtracting 750 (blank) from 630 and 633 values, and accounting for path length (0.584 cm)
chla.data.0708$abs.630.corr <- (chla.data.0708$`Chl.630` - Blank.0708$blk.avg) / 0.584
chla.data.0708$abs.663.corr <- (chla.data.0708$`Chl.663` - Blank.0708$`blk.avg`) / 0.584
chla.data.0716$abs.630.corr <- (chla.data.0716$`Chl.630` - Blank.0716$blk.avg) / 0.584
chla.data.0716$abs.663.corr <- (chla.data.0716$`Chl.663` - Blank.0716$`blk.avg`) / 0.584
# Combining Datasets
chla.data.all <- rbind(chla.data.0708, chla.data.0716)
# Chlorp0hyll A concentration equation
chla.data.all$chlA.ug.sample <- 11.43*chla.data.all$abs.663.corr - 0.64*chla.data.all$abs.630.corr
# Chlorophyll C2 concentration equation
chla.data.all$chlC2.ug.sample <- 27.09*chla.data.all$abs.630.corr - 3.63*chla.data.all$abs.663.corr
# Attaching colony metadata
Chla.data.meta <- merge(chla.data.all, master.meta, by = "Vial")
# Standardization
Chla.data.meta$ChlA.ugcm2 <- (Chla.data.meta$chlA.ug.sample * Chla.data.meta$Homogenate_Vol.mL)/Chla.data.meta$Surface_Area_cm2 #Calculating concentration
Chla.data.meta$ChlC2.ugcm2 <- (Chla.data.meta$chlC2.ug.sample * Chla.data.meta$Homogenate_Vol.mL)/Chla.data.meta$Surface_Area_cm2 #Calculating concentration
# Removing well A9-20210716 because of pipette error
Chla.data.meta.clean <- Chla.data.meta %>%
filter(date.well != "20210716-A9")
# Summarize per vial
Chla.sum <- summarySE(Chla.data.meta.clean, measurevar="ChlA.ugcm2", groupvars=c("Vial", "Fragment.ID.y", "Day", "Group"))
Chla.sum2 <- Chla.sum %>%
dplyr::select(Fragment.ID.y, Day, Group, ChlA.ugcm2)
ChlC2.sum <- summarySE(Chla.data.meta.clean, measurevar="ChlC2.ugcm2", groupvars=c("Vial", "Fragment.ID.y", "Day", "Group"))
ChlC2.sum2 <- ChlC2.sum %>%
dplyr::select(Fragment.ID.y, Day, Group, ChlC2.ugcm2)
Chl.final <- merge(Chla.sum2, ChlC2.sum2, by = c("Fragment.ID.y", "Day", "Group"))
Chl.final$Day <- as.factor(Chl.final$Day)
Chl.final$Group <- as.factor(Chl.final$Group)
names(Chl.final)[1] <- "Fragment.ID"
write.csv(Chl.final, "../output/Physiology/Chl.Calc.csv")Total Protein
TP.well.meta <- read.csv("../data/Physiology/Protein/Protein_Well_Meta.csv")
raw_20210701 <- read.csv("../data/Physiology/Protein/20210701_Protein.csv")
raw_20210702 <- read.csv("../data/Physiology/Protein/20210702_Protein.csv")
raw_20210706 <- read.csv("../data/Physiology/Protein/20210706_Protein.csv")
raw_20210707 <- read.csv("../data/Physiology/Protein/20210707_Protein.csv")
# Adding Run numbers into datasets
raw_20210701$run <- "20210701"
raw_20210702$run <- "20210702"
raw_20210706$run <- "20210706"
raw_20210707$run <- "20210707"
# Make a unique column for merging
raw_20210701$run.well <- paste(raw_20210701$run, raw_20210701$Well, sep = "-")
raw_20210702$run.well <- paste(raw_20210702$run, raw_20210702$Well, sep = "-")
raw_20210706$run.well <- paste(raw_20210706$run, raw_20210706$Well, sep = "-")
raw_20210707$run.well <- paste(raw_20210707$run, raw_20210707$Well, sep = "-")
TP.well.meta$run.well <- paste(TP.well.meta$Date, TP.well.meta$Well, sep = "-")
# Merge with metadata
TP.20210701 <- merge(raw_20210701, TP.well.meta, by = "run.well")
TP.20210702 <- merge(raw_20210702, TP.well.meta, by = "run.well")
TP.20210706 <- merge(raw_20210706, TP.well.meta, by = "run.well")
TP.20210707 <- merge(raw_20210707, TP.well.meta, by = "run.well")
# Subtract blanks means for each run
TP.standardblank.01 <- TP.20210701 %>%
filter(Sample.Type == "Blank") %>%
summarise(blk.avg = mean(X562))
TP.20210701$abs.corr <- TP.20210701$X562 - TP.standardblank.01$blk.avg
TP.standardblank.02 <- TP.20210702 %>%
filter(Sample.Type == "Blank") %>%
summarise(blk.avg = mean(X562))
TP.20210702$abs.corr <- TP.20210702$X562 - TP.standardblank.02$blk.avg
TP.standardblank.06 <- TP.20210706 %>%
filter(Sample.Type == "Blank") %>%
summarise(blk.avg = mean(X562))
TP.20210706$abs.corr <- TP.20210706$X562 - TP.standardblank.06$blk.avg
TP.standardblank.07 <- TP.20210707 %>%
filter(Sample.Type == "Blank") %>%
summarise(blk.avg = mean(X562))
TP.20210707$abs.corr <- TP.20210707$X562 - TP.standardblank.07$blk.avg
# Run standards
TP.standard.01 <- TP.20210701 %>%
filter(Sample.Type == "Standard")
TP.plot.S1<- ggplot(data = TP.standard.01, aes(x=Concentration, y=abs.corr))+
ylab("Absorbance (nm)")+ xlab("Concentration") +
geom_point()+
geom_smooth(method = "lm") +
stat_regline_equation(label.y = 2.0, aes(label = ..eq.label..)) +
stat_regline_equation(label.y = 1.75, aes(label = ..rr.label..)) +
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
TP.plot.S1
TP.lmstandard.01 <- lm (Concentration ~ abs.corr, data = TP.standard.01)
TP.lmsummary.01 <- summary(TP.lmstandard.01)
TP.standard.02 <- TP.20210702 %>%
filter(Sample.Type == "Standard")
TP.plot.S2<- ggplot(data = TP.standard.02, aes(x=Concentration, y=abs.corr))+
ylab("Absorbance (nm)")+ xlab("Concentration") +
geom_point()+
geom_smooth(method = "lm") +
stat_regline_equation(label.y = 2.0, aes(label = ..eq.label..)) +
stat_regline_equation(label.y = 1.75, aes(label = ..rr.label..)) +
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
TP.plot.S2
TP.lmstandard.02 <- lm (Concentration ~ abs.corr, data = TP.standard.02)
TP.lmsummary.02 <- summary(TP.lmstandard.02)
TP.standard.06 <- TP.20210706 %>%
filter(Sample.Type == "Standard")
TP.plot.S6<- ggplot(data = TP.standard.06, aes(x=Concentration, y=abs.corr))+
ylab("Absorbance (nm)")+ xlab("Concentration") +
geom_point()+
geom_smooth(method = "lm") +
stat_regline_equation(label.y = 2.0, aes(label = ..eq.label..)) +
stat_regline_equation(label.y = 1.75, aes(label = ..rr.label..)) +
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
TP.plot.S6
TP.lmstandard.06 <- lm (Concentration ~ abs.corr, data = TP.standard.06)
TP.lmsummary.06 <- summary(TP.lmstandard.06)
TP.standard.07 <- TP.20210707 %>%
filter(Sample.Type == "Standard")
TP.plot.S7<- ggplot(data = TP.standard.07, aes(x=Concentration, y=abs.corr))+
ylab("Absorbance (nm)")+ xlab("Concentration") +
geom_point()+
geom_smooth(method = "lm") +
stat_regline_equation(label.y = 2.0, aes(label = ..eq.label..)) +
stat_regline_equation(label.y = 1.75, aes(label = ..rr.label..)) +
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
TP.plot.S7
TP.lmstandard.07 <- lm (Concentration ~ abs.corr, data = TP.standard.07)
TP.lmsummary.07 <- summary(TP.lmstandard.07)
# Extrapolate concentration values
TP.Sample.01 <- TP.20210701 %>% #subsetting Samples
filter(Sample.Type == "Sample")
TP.Sample.01$ConcentrationS <- predict(TP.lmstandard.01, newdata = TP.Sample.01) #using model to get concentration
TP.Sample.02 <- TP.20210702 %>% #subsetting Samples
filter(Sample.Type == "Sample")
TP.Sample.02$ConcentrationS <- predict(TP.lmstandard.02, newdata = TP.Sample.02) #using model to get concentration
TP.Sample.06 <- TP.20210706 %>% #subsetting Samples
filter(Sample.Type == "Sample")
TP.Sample.06$ConcentrationS <- predict(TP.lmstandard.06, newdata = TP.Sample.06) #using model to get concentration
TP.Sample.07 <- TP.20210707 %>% #subsetting Samples
filter(Sample.Type == "Sample")
TP.Sample.07$ConcentrationS <- predict(TP.lmstandard.07, newdata = TP.Sample.07) #using model to get concentration
# Row Bind DFs together
TP.all <- rbind(TP.Sample.01, TP.Sample.02, TP.Sample.06, TP.Sample.07)
# Adding metadata
TP.all.meta <- merge(TP.all, master.meta, by = "Vial")
# Standardization
TP.all.meta$Total.Protein.ugcm2 <- (TP.all.meta$ConcentrationS * TP.all.meta$Homogenate_Vol.mL*1.45)/TP.all.meta$Surface_Area_cm2 #Calculating concentration. 1.45 = Dilution factor of acid+base in sample
TP.all.meta$Total.Protein.mgcm2 <- TP.all.meta$Total.Protein.ugcm2/1000
# Summarize per vial
TP.sum <- summarySE(TP.all.meta, measurevar="Total.Protein.mgcm2", groupvars=c("Vial", "Fragment.ID.y", "Day", "Group", "Type"))
TP.sum2 <- TP.sum %>%
dplyr::select(Fragment.ID.y, Day, Group, Type, Total.Protein.mgcm2)
# Converting long to wide format
TP.final<- TP.sum2 %>%
pivot_wider(names_from = Type, values_from = Total.Protein.mgcm2)
# Calculating Host to Symbiont Ratio
TP.final$HostSymRatioProtein <- TP.final$Coral / TP.final$Symbiont
# Renaming columns
TP.final2 <- TP.final %>%
dplyr::rename(Coral_Protein_mgcm2 = Coral,
Symbiont_Protein_mgcm2 = Symbiont)
TP.final2$Day <- as.factor(TP.final2$Day)
TP.final2$Group <- as.factor(TP.final2$Group)
names(TP.final2)[1] <- "Fragment.ID"
write.csv(TP.final2, "../output/Physiology/Protein.Calc.csv")Carbohydrate
# Read in datafiles
carb.data.0715 <- read.csv("../data/Physiology/Carbohydrate/20210715_Carb.csv")
carb.data.0716 <- read.csv("../data/Physiology/Carbohydrate/20210716_Carb.csv")
carb.data.0719_1 <- read.csv("../data/Physiology/Carbohydrate/20210719-1_Carb.csv")
carb.data.0719_2 <- read.csv("../data/Physiology/Carbohydrate/20210719-2_Carb.csv")
carb.meta <- read.csv("../data/Physiology/Carbohydrate/Carb_Meta.csv")
# Adding a date column
carb.data.0715$Date <- "20210715"
carb.data.0716$Date <- "20210716"
carb.data.0719_1$Date <- "20210719-1"
carb.data.0719_2$Date <- "20210719-2"
# Making unique columns by date/well
carb.data.0715$date.well <- paste(carb.data.0715$Date, carb.data.0715$Well, sep = "-")
carb.data.0716$date.well <- paste(carb.data.0716$Date, carb.data.0716$Well, sep = "-")
carb.data.0719_1$date.well <- paste(carb.data.0719_1$Date, carb.data.0719_1$Well, sep = "-")
carb.data.0719_2$date.well <- paste(carb.data.0719_2$Date, carb.data.0719_2$Well, sep = "-")
carb.meta$date.well <- paste(carb.meta$Date, carb.meta$Well, sep = "-")
# Merging Metadata
carb.data.meta.0715 <- merge(carb.data.0715, carb.meta, by = "date.well") #has standard
carb.data.meta.0716 <- merge(carb.data.0716, carb.meta, by = "date.well") #use 0715 standard
carb.data.meta.0719_1 <- merge(carb.data.0719_1, carb.meta, by = "date.well") #has standard
carb.data.meta.0719_2 <- merge(carb.data.0719_2, carb.meta, by = "date.well") #use 0719-1 standard
# Blank correction for each corresponding run
Blank.0715 <- carb.data.meta.0715 %>%
filter(Sample.Type == "Blank") %>%
summarise(blk.avg = mean(X485))
carb.data.meta.0715$abs.corr <- carb.data.meta.0715$X485 - Blank.0715$blk.avg
carb.data.meta.0716$abs.corr <- carb.data.meta.0716$X485 - Blank.0715$blk.avg
Blank.0719 <- carb.data.meta.0719_1 %>%
filter(Sample.Type == "Blank") %>%
summarise(blk.avg = mean(X485))
carb.data.meta.0719_1$abs.corr <- carb.data.meta.0719_1$X485 - Blank.0719$blk.avg
carb.data.meta.0719_2$abs.corr <- carb.data.meta.0719_2$X485 - Blank.0719$blk.avg
# Standard curve
Standard.0715 <- carb.data.meta.0715 %>%
filter(Sample.Type == "Standard")
Standard.0715.plot <- ggplot(data = Standard.0715, aes(x=Concentration, y=abs.corr))+
ylab("Absorbance (nm)")+ xlab("Carbohydrate (mg/mL)") +
geom_point()+
geom_smooth(method = "lm") +
stat_regline_equation(label.y = 1.0, aes(label = ..eq.label..)) +
stat_regline_equation(label.y = 0.75, aes(label = ..rr.label..)) +
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Standard.0715.plot
lmstandard.0715 <- lm (Concentration ~ abs.corr, data = Standard.0715)
lmsummary.0715 <- summary(lmstandard.0715)
Standard.0719 <- carb.data.meta.0719_1 %>%
filter(Sample.Type == "Standard")
Standard.0719.plot <- ggplot(data = Standard.0719, aes(x=Concentration, y=abs.corr))+
ylab("Absorbance (nm)")+ xlab("Carbohydrate (mg/mL)") +
geom_point()+
geom_smooth(method = "lm") +
stat_regline_equation(label.y = 1.0, aes(label = ..eq.label..)) +
stat_regline_equation(label.y = 0.75, aes(label = ..rr.label..)) +
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Standard.0719.plot
lmstandard.0719 <- lm (Concentration ~ abs.corr, data = Standard.0719)
lmsummary.0719 <- summary(lmstandard.0719)
# Obtaining concentration values from standard curve
Samples.0715 <- carb.data.meta.0715 %>% #subsetting Samples
filter(Sample.Type == "Sample")
Samples.0715$Concentration <- predict(lmstandard.0715, newdata = Samples.0715) #using model to get concentration
Samples.0716 <- carb.data.meta.0716 %>% #subsetting Samples
filter(Sample.Type == "Sample")
Samples.0716$Concentration <- predict(lmstandard.0715, newdata = Samples.0716) #using model to get concentration
Samples.0719_1 <- carb.data.meta.0719_1 %>% #subsetting Samples
filter(Sample.Type == "Sample")
Samples.0719_1$Concentration <- predict(lmstandard.0719, newdata = Samples.0719_1) #using model to get concentration
Samples.0719_2 <- carb.data.meta.0719_2 %>% #subsetting Samples
filter(Sample.Type == "Sample")
Samples.0719_2$Concentration <- predict(lmstandard.0719, newdata = Samples.0719_2) #using model to get concentration
# Merging metadata
Samples.carb.all <- rbind(Samples.0715, Samples.0716, Samples.0719_1, Samples.0719_2)
samples.carb.meta <- merge(Samples.carb.all, master.meta, by = "Vial")
# Accounting for dilution factor (1000/10) and Normalizing to homogenate volume and surface area
samples.carb.meta$Carb.mgcm2 <- (samples.carb.meta$Concentration * samples.carb.meta$Homogenate_Vol.mL * 10)/samples.carb.meta$Surface_Area_cm2
#Summarize per vial
carb.sum <- summarySE(samples.carb.meta, measurevar="Carb.mgcm2", groupvars=c("Vial", "Fragment.ID.y", "Day", "Group", "Type"))
carb.sum2 <- carb.sum %>%
dplyr::select(Fragment.ID.y, Day, Group, Type, Carb.mgcm2)
# Converting long to wide format
Carb.final<- carb.sum2 %>%
pivot_wider(names_from = Type, values_from = Carb.mgcm2)
# Calculating Host to Symbiont Ratio
Carb.final$HostSymRatioCarb <- Carb.final$Coral / Carb.final$Symbiont
# Renaming columns
Carb.final2 <- Carb.final %>%
dplyr::rename(Coral_Carb_mgcm2 = Coral,
Symbiont_Carb_mgcm2 = Symbiont)
Carb.final2$Day <- as.factor(Carb.final2$Day)
Carb.final2$Group <- as.factor(Carb.final2$Group)
names(Carb.final2)[1] <- "Fragment.ID"
write.csv(Carb.final2, "../output/Physiology/Carb.Calc.csv")Master Datasheet and Normalizations
resp_dataset <- read.csv("../output/Physiology/Resp.Calc.csv")
carb_dataset <- read.csv("../output/Physiology/Carb.Calc.csv")
prot_dataset <- read.csv("../output/Physiology/Protein.Calc.csv")
sym_dataset <- read.csv("../output/Physiology/Zoox.Calc.csv")
chl_dataset <- read.csv("../output/Physiology/Chl.Calc.csv")
frag.meta <- read.csv("../data/Physiology/Airbrush_Metadata.csv")
#selecting SA column
frag.sa <- frag.meta %>%
dplyr::select(Fragment.ID, Day, Group, Surface_Area_cm2)
# Joining datasets
dfs <- list(resp_dataset,
carb_dataset,
prot_dataset,
sym_dataset,
chl_dataset,
frag.sa)
master <- join_all(dfs, by=c("Fragment.ID", "Day", "Group")) %>%
dplyr::select(-ends_with("X"))
# Normalization to per sym cell
master$Cells <- master$Cells.cm2.x6 * master$Surface_Area_cm2 * 1000000 # getting absolute number of cells
master$ChlA.ugcell <- ((master$ChlA.ugcm2*master$Surface_Area_cm2) / (master$Cells))
master$ChlC2.ugcell <- ((master$ChlC2.ugcm2*master$Surface_Area_cm2) / (master$Cells))
master$Carb.mgcell <- ((master$Symbiont_Carb_mgcm2*master$Surface_Area_cm2) / (master$Cells))
master$Protein.mgcell <- ((master$Symbiont_Protein_mgcm2*master$Surface_Area_cm2) / (master$Cells))
master$Day <- as.factor(master$Day)
master$Group <- as.factor(master$Group)
master$Fragment.ID <- as.factor(master$Fragment.ID)
master## Fragment.ID Day Group Pnet_umol.cm2.hr Pgross_umol.cm2.hr
## 1 R11 52 Mortality -0.23189241 0.1020934734
## 2 R11 0 Mortality 0.50582731 1.1868464067
## 3 R11 37 Mortality -0.36245920 0.0033265282
## 4 R17 52 Control 0.26802023 0.6920767697
## 5 R17 37 Control 0.71085019 1.3291667409
## 6 R17 0 Control 0.41877850 1.1186333864
## 7 R19 52 Bleached -0.41907750 0.0567272258
## 8 R19 0 Bleached 0.38354936 1.1095961967
## 9 R19 37 Bleached -0.11848265 0.1411971380
## 10 R20 52 Bleached -0.35947860 -0.0003639834
## 11 R20 37 Bleached -0.16116833 0.1184719656
## 12 R20 0 Bleached 0.16410711 0.6424367190
## 13 R23 52 Control -0.04329761 0.3978362514
## 14 R23 0 Control 0.24238553 0.7307221520
## 15 R23 37 Control 0.23486158 0.8538006108
## 16 R26 52 Mortality -0.16624709 0.2905505959
## 17 R26 37 Mortality -0.32821468 0.1267031397
## 18 R26 0 Mortality 0.15241072 0.7662224634
## 19 R28 52 Mortality -0.19605011 0.3981000459
## 20 R28 0 Mortality 0.21938199 1.0187704774
## 21 R28 37 Mortality -0.19076104 0.1604887254
## 22 R29 52 Bleached -0.20851651 0.1901868201
## 23 R29 37 Bleached -0.18425232 0.1143434084
## 24 R29 0 Bleached 0.32886894 1.0688889329
## 25 R32 37 Control 0.19991505 0.5733927695
## 26 R32 52 Control 0.12338534 0.5100397489
## 27 R32 0 Control 0.03985412 0.7318985087
## 28 R35 52 Mortality -0.24541776 0.2131728043
## 29 R35 37 Mortality -0.17148385 0.2335203154
## 30 R35 0 Mortality 0.02967193 0.8438741572
## 31 R36 52 Mortality -0.37237629 0.0934852282
## 32 R36 37 Mortality -0.44603538 -0.0156581586
## 33 R36 0 Mortality 0.08499423 0.6582026486
## 34 R37 37 Bleached -0.34553509 0.1061178335
## 35 R37 52 Bleached -0.30877729 0.0663087355
## 36 R37 0 Bleached 0.25089923 1.1171429486
## 37 R40 37 Control 0.30772274 0.6987146249
## 38 R40 0 Control 0.22809026 0.9602083084
## 39 R40 52 Control 0.01742511 0.5202652851
## 40 R7 37 Control 0.73880159 1.2836098810
## 41 R7 0 Control 0.15738175 0.8461748059
## 42 R7 52 Control 0.15520373 0.7734739299
## 43 R8 37 Bleached -0.44205832 0.1024291844
## 44 R8 0 Bleached 0.26755533 0.7632090733
## 45 R8 52 Bleached -0.28186064 0.0627559505
## abs.Rdark_umol.cm2.hr PR Coral_Carb_mgcm2 Symbiont_Carb_mgcm2
## 1 0.3339859 0.305682003 6.849361 1.7643484
## 2 0.6810191 1.742750543 11.308695 3.1202253
## 3 0.3657857 0.009094199 12.095280 3.8631560
## 4 0.4240565 1.632038911 6.839514 2.9490419
## 5 0.6183166 2.149654148 5.563662 3.0202852
## 6 0.6998549 1.598379057 7.566091 2.7553698
## 7 0.4758047 0.119223754 10.949188 3.7515966
## 8 0.7260468 1.528270817 9.239969 2.1188079
## 9 0.2596798 0.543735581 11.808212 3.0126430
## 10 0.3591146 -0.001013558 3.501538 2.4675249
## 11 0.2796403 0.423658414 11.267394 3.7730921
## 12 0.4783296 1.343083741 13.414633 3.3379630
## 13 0.4411339 0.901849265 7.433731 3.1970812
## 14 0.4883366 1.496349292 7.162866 3.0593058
## 15 0.6189390 1.379458335 9.196945 3.1675563
## 16 0.4567977 0.636059691 4.323523 2.5349244
## 17 0.4549178 0.278518741 9.142666 3.8381270
## 18 0.6138117 1.248302056 8.903752 3.2006537
## 19 0.5941502 0.670032725 5.728313 2.3687238
## 20 0.7993885 1.274437258 9.784738 1.3047815
## 21 0.3512498 0.456907706 6.014000 2.1604203
## 22 0.3987033 0.477013371 8.957309 2.5973352
## 23 0.2985957 0.382937186 8.994702 2.0654242
## 24 0.7400200 1.444405471 11.828278 3.2892179
## 25 0.3734777 1.535279733 11.895859 4.2947185
## 26 0.3866544 1.319110125 7.044523 2.7746750
## 27 0.6920444 1.057588969 11.424910 4.5526846
## 28 0.4585906 0.464843411 7.295854 3.8457757
## 29 0.4050042 0.576587447 5.638928 3.6298139
## 30 0.8142022 1.036442949 12.973316 7.4011411
## 31 0.4658615 0.200671712 7.707399 3.0474493
## 32 0.4303772 -0.036382406 7.803940 0.4781953
## 33 0.5732084 1.148278058 10.560248 2.6024627
## 34 0.4516529 0.234954384 13.123489 5.3589500
## 35 0.3750860 0.176782740 6.351416 2.0801262
## 36 0.8662437 1.289640462 10.656405 3.3870259
## 37 0.3909919 1.787031026 7.585425 2.6984209
## 38 0.7321180 1.311548472 7.970075 3.6814867
## 39 0.5028402 1.034653372 7.523901 3.3213336
## 40 0.5448083 2.356076265 9.237811 4.3413046
## 41 0.6887931 1.228489171 6.444712 2.4862677
## 42 0.6182702 1.251028970 6.673854 2.6173143
## 43 0.5444875 0.188120358 5.671352 2.4961686
## 44 0.4956537 1.539802914 10.097315 3.7617888
## 45 0.3446166 0.182103684 6.963212 3.5262658
## HostSymRatioCarb Coral_Protein_mgcm2 Symbiont_Protein_mgcm2
## 1 3.882091 0.7777801 1.0472301
## 2 3.624320 1.2378482 1.7432650
## 3 3.130932 1.8390078 1.5447205
## 4 2.319233 0.8736717 1.1589387
## 5 1.842098 0.6569932 1.0931865
## 6 2.745944 1.1305265 1.2609914
## 7 2.918541 0.8252890 1.2452956
## 8 4.360928 0.9373076 0.8384386
## 9 3.919552 1.1680675 0.9544774
## 10 1.419049 1.1041223 1.4536745
## 11 2.986249 1.5541780 1.6921869
## 12 4.018808 1.4416994 1.4976326
## 13 2.325162 0.8660496 1.1289232
## 14 2.341337 1.0767521 1.2553001
## 15 2.903483 0.9825597 1.3234874
## 16 1.705583 0.7562860 1.2217228
## 17 2.382065 1.5382525 1.9977511
## 18 2.781854 1.0429995 1.4323859
## 19 2.418312 0.5977390 0.8906722
## 20 7.499139 1.1932539 1.3533129
## 21 2.783718 0.9747326 0.9724740
## 22 3.448653 1.1988134 1.5509649
## 23 4.354893 1.1360409 1.2085604
## 24 3.596076 1.5561040 1.6477717
## 25 2.769881 1.4273253 1.4083638
## 26 2.538864 1.1112201 1.3982868
## 27 2.509489 1.7837592 2.2583977
## 28 1.897109 1.2431900 1.7403353
## 29 1.553503 1.1883370 1.3598986
## 30 1.752881 1.6339445 1.3969345
## 31 2.529131 1.1020099 1.4598403
## 32 16.319564 0.9821318 0.8657490
## 33 4.057790 1.1750891 1.1892187
## 34 2.448892 1.6609407 2.0817057
## 35 3.053380 1.1211654 1.2873024
## 36 3.146242 1.7877324 1.7211346
## 37 2.811061 0.8884828 1.1446421
## 38 2.164907 0.9103872 1.0356749
## 39 2.265325 0.9648052 1.4423972
## 40 2.127888 0.8450959 1.1170357
## 41 2.592123 0.9654668 1.3912152
## 42 2.549886 0.6772846 1.0224350
## 43 2.272023 0.8731718 1.2980929
## 44 2.684179 1.0600605 1.5346158
## 45 1.974670 0.8337565 1.2834567
## HostSymRatioProtein Cells.cm2.x6 ChlA.ugcm2 ChlC2.ugcm2 Surface_Area_cm2
## 1 0.7427022 0.1540965 0.3275101 0.09132213 10.999
## 2 0.7100746 1.4437051 7.4072233 2.48951700 4.358
## 3 1.1905117 0.4190049 1.3837000 0.49063696 2.970
## 4 0.7538550 0.8260314 2.6574699 0.83689365 5.478
## 5 0.6009892 1.3826249 5.6995893 1.64190686 8.869
## 6 0.8965378 2.0221236 10.3365517 3.33457116 9.462
## 7 0.6627254 0.1963828 1.3405398 0.38270509 4.366
## 8 1.1179204 1.1433489 4.2424029 1.36950514 8.134
## 9 1.2237769 0.3792668 1.0811195 1.01473339 2.373
## 10 0.7595389 0.2994328 0.9156895 1.76202006 7.457
## 11 0.9184435 0.4793840 3.5937723 1.05426883 3.355
## 12 0.9626523 1.6895325 8.2288377 2.63920650 4.346
## 13 0.7671466 0.7009038 4.8898627 5.37157161 5.053
## 14 0.8577646 1.3687119 5.1644179 1.55973806 5.851
## 15 0.7424020 1.1838762 2.0581962 0.25032277 4.329
## 16 0.6190324 0.4080261 1.2990092 0.27255717 10.416
## 17 0.7699920 0.4046492 1.5966270 0.50438075 4.438
## 18 0.7281554 1.3479952 6.0547269 2.04455744 5.570
## 19 0.6711100 0.1033754 0.7077547 0.17061524 8.733
## 20 0.8817280 1.6370114 9.2615161 7.27550581 8.374
## 21 1.0023225 0.1921334 0.5019107 0.21612215 6.130
## 22 0.7729468 0.2255075 0.2815202 0.02862173 5.851
## 23 0.9399951 0.1618713 0.9437220 0.93641702 5.028
## 24 0.9443687 1.0647622 3.5832597 1.41391240 3.518
## 25 1.0134635 1.0270481 4.1029036 1.38907203 2.779
## 26 0.7947011 1.1101703 2.7164310 1.05396663 9.448
## 27 0.7898340 2.4036319 13.8831786 4.10523954 2.244
## 28 0.7143393 0.8808811 1.2484700 0.60104648 4.846
## 29 0.8738423 0.3630730 1.3484589 0.46397694 3.870
## 30 1.1696644 1.8293839 6.1702672 1.85880997 2.110
## 31 0.7548839 0.3418154 0.7865949 0.27474975 5.266
## 32 1.1344302 0.4723115 0.7238693 0.27262691 5.497
## 33 0.9881186 1.3758214 4.9521489 1.59004149 3.348
## 34 0.7978749 0.5588378 4.1108403 1.12810839 2.144
## 35 0.8709418 0.1145972 0.5022336 0.14783410 7.490
## 36 1.0386941 1.4702543 5.2101219 1.63831837 3.670
## 37 0.7762101 0.8459411 3.5594404 1.15392072 8.196
## 38 0.8790280 1.7612390 9.0560350 2.84515065 1.824
## 39 0.6688900 0.9430216 2.6613505 0.80002162 5.037
## 40 0.7565523 1.1229514 5.1353209 1.67873098 4.434
## 41 0.6939737 1.2677780 4.9487060 1.40710187 8.578
## 42 0.6624232 0.9563789 2.0493608 0.73343085 10.809
## 43 0.6726574 0.1335096 1.0028003 0.39763902 6.429
## 44 0.6907660 2.1099318 11.5075025 3.63966661 4.334
## 45 0.6496180 0.2329879 1.3589985 0.44224326 5.532
## Cells ChlA.ugcell ChlC2.ugcell Carb.mgcell Protein.mgcell
## 1 1694907.4 2.125357e-06 5.926295e-07 1.144963e-05 6.795937e-06
## 2 6291666.7 5.130704e-06 1.724394e-06 2.161262e-06 1.207494e-06
## 3 1244444.4 3.302348e-06 1.170958e-06 9.219836e-06 3.686641e-06
## 4 4525000.0 3.217154e-06 1.013150e-06 3.570133e-06 1.403020e-06
## 5 12262500.0 4.122296e-06 1.187529e-06 2.184457e-06 7.906602e-07
## 6 19133333.3 5.111731e-06 1.649044e-06 1.362612e-06 6.235976e-07
## 7 857407.4 6.826156e-06 1.948771e-06 1.910349e-05 6.341164e-06
## 8 9300000.0 3.710506e-06 1.197802e-06 1.853159e-06 7.333182e-07
## 9 900000.0 2.850552e-06 2.675514e-06 7.943335e-06 2.516639e-06
## 10 2232870.4 3.058080e-06 5.884526e-06 8.240663e-06 4.854760e-06
## 11 1608333.3 7.496646e-06 2.199216e-06 7.870709e-06 3.529919e-06
## 12 7342708.3 4.870482e-06 1.562093e-06 1.975673e-06 8.864183e-07
## 13 3541666.7 6.976511e-06 7.663779e-06 4.561370e-06 1.610668e-06
## 14 8008333.3 3.773196e-06 1.139566e-06 2.235171e-06 9.171398e-07
## 15 5125000.0 1.738523e-06 2.114434e-07 2.675581e-06 1.117927e-06
## 16 4250000.0 3.183642e-06 6.679895e-07 6.212652e-06 2.994227e-06
## 17 1795833.3 3.945706e-06 1.246464e-06 9.485072e-06 4.936995e-06
## 18 7508333.3 4.491653e-06 1.516739e-06 2.374381e-06 1.062605e-06
## 19 902777.8 6.846449e-06 1.650443e-06 2.291379e-05 8.615897e-06
## 20 13708333.3 5.657576e-06 4.444383e-06 7.970510e-07 8.266973e-07
## 21 1177777.8 2.612303e-06 1.124855e-06 1.124438e-05 5.061452e-06
## 22 1319444.4 1.248385e-06 1.269214e-07 1.151773e-05 6.877664e-06
## 23 813888.9 5.830076e-06 5.784948e-06 1.275967e-05 7.466181e-06
## 24 3745833.3 3.365315e-06 1.327914e-06 3.089157e-06 1.547549e-06
## 25 2854166.7 3.994851e-06 1.352490e-06 4.181614e-06 1.371273e-06
## 26 10488888.9 2.446860e-06 9.493738e-07 2.499324e-06 1.259525e-06
## 27 5393750.0 5.775917e-06 1.707932e-06 1.894086e-06 9.395772e-07
## 28 4268750.0 1.417297e-06 6.823242e-07 4.365828e-06 1.975676e-06
## 29 1405092.6 3.714016e-06 1.277916e-06 9.997476e-06 3.745524e-06
## 30 3860000.0 3.372866e-06 1.016085e-06 4.045701e-06 7.636093e-07
## 31 1800000.0 2.301227e-06 8.037957e-07 8.915482e-06 4.270844e-06
## 32 2596296.3 1.532610e-06 5.772185e-07 1.012458e-06 1.833004e-06
## 33 4606250.0 3.599413e-06 1.155703e-06 1.891570e-06 8.643699e-07
## 34 1198148.1 7.356053e-06 2.018669e-06 9.589456e-06 3.725063e-06
## 35 858333.3 4.382597e-06 1.290032e-06 1.815163e-05 1.123328e-05
## 36 5395833.3 3.543688e-06 1.114310e-06 2.303701e-06 1.170637e-06
## 37 6933333.3 4.207669e-06 1.364067e-06 3.189845e-06 1.353099e-06
## 38 3212500.0 5.141855e-06 1.615426e-06 2.090282e-06 5.880377e-07
## 39 4750000.0 2.822152e-06 8.483598e-07 3.522012e-06 1.529548e-06
## 40 4979166.7 4.573057e-06 1.494927e-06 3.865977e-06 9.947320e-07
## 41 10875000.0 3.903448e-06 1.109896e-06 1.961122e-06 1.097365e-06
## 42 10337500.0 2.142833e-06 7.668831e-07 2.736692e-06 1.069069e-06
## 43 858333.3 7.511072e-06 2.978355e-06 1.869655e-05 9.722842e-06
## 44 9144444.4 5.453969e-06 1.725016e-06 1.782896e-06 7.273296e-07
## 45 1288888.9 5.832915e-06 1.898139e-06 1.513498e-05 5.508685e-06Boxplots and statistics
###############################
TP_Coral_Box <- ggplot(master, aes(x=Day, y=Coral_Protein_mgcm2, fill = Group)) +
geom_boxplot(width=.5, outlier.shape= NA, position = position_dodge(width = 0.5)) +
stat_summary(fun=mean, geom="line", aes(group=Group, color = Group), position = position_dodge(width = 0.5)) +
# stat_summary(fun=mean, geom="point")
geom_point(pch = 21, position=position_jitterdodge(dodge.width=0.5)) +
# ylim(0,0.5) +
scale_fill_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_x_discrete(labels=c("0" = "Day 0", "37" = "Day 37", "52" = "Day 52")) +
xlab("Timepoint") + ylab(expression("Total Protein " (mg~cm^{-2}))) + #Axis titles
theme_bw() + theme(panel.border = element_rect(color="black", fill=NA, size=0.75), panel.grid.major = element_blank(), #Makes background theme white
panel.grid.minor = element_blank(), axis.line = element_blank()) +
theme(axis.text = element_text(size = 15, color = "black"),
axis.title = element_text(size = 18, color = "black")) + ggtitle("Coral Total Protein")
TP_Coral_Box
TP_Coral_lmer <- lmer(Coral_Protein_mgcm2~Group*Day+(1|Fragment.ID), data = master)
# qqnorm(resid(TP_Coral_lmer))
# qqline(resid(TP_Coral_lmer))
#
# boxplot(resid(TP_Coral_lmer)~master$Group)
# boxplot(resid(TP_Coral_lmer)~master$Day)
anova(TP_Coral_lmer, type="II")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.10064 0.05032 2 12 1.0667 0.374642
## Day 0.86013 0.43006 2 24 9.1168 0.001134 **
## Group:Day 0.16510 0.04127 4 24 0.8750 0.493418
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#capture.output(anova(TP_Coral_lmer, type="II"), file = "../output/Statistics/TP_Coral_lmer.csv")
###############################
TP_Sym_SA_Box <- ggplot(master, aes(x=Day, y=Symbiont_Protein_mgcm2, fill = Group)) +
geom_boxplot(width=.5, outlier.shape= NA, position = position_dodge(width = 0.5)) +
stat_summary(fun=mean, geom="line", aes(group=Group, color = Group), position = position_dodge(width = 0.5)) +
# stat_summary(fun=mean, geom="point")
geom_point(pch = 21, position=position_jitterdodge(dodge.width=0.5)) +
# ylim(0,0.5) +
scale_fill_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_x_discrete(labels=c("0" = "Day 0", "37" = "Day 37", "52" = "Day 52")) +
xlab("Timepoint") + ylab(expression("Total Protein " (mg~cm^{-2}))) + #Axis titles
theme_bw() + theme(panel.border = element_rect(color="black", fill=NA, size=0.75), panel.grid.major = element_blank(), #Makes background theme white
panel.grid.minor = element_blank(), axis.line = element_blank()) +
theme(axis.text = element_text(size = 15, color = "black"),
axis.title = element_text(size = 18, color = "black")) + ggtitle("Symbiont Total Protein (per cm2)")
TP_Sym_SA_Box
TP_Sym_lmer <- lmer(Symbiont_Protein_mgcm2~Group*Day+(1|Fragment.ID), data = master)
# qqnorm(resid(TP_Sym_lmer))
# qqline(resid(TP_Sym_lmer))
#
# boxplot(resid(TP_Sym_lmer)~master$Group)
# boxplot(resid(TP_Sym_lmer)~master$Day)
anova(TP_Sym_lmer, type="II")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.060275 0.030138 2 12 0.3625 0.7033
## Day 0.171463 0.085732 2 24 1.0312 0.3718
## Group:Day 0.065453 0.016363 4 24 0.1968 0.9376#capture.output(anova(TP_Sym_lmer, type="II"), file = "../output/Statistics/TP_Sym_lmer.csv")
###############################
TP_Sym_Cell_Box <- ggplot(master, aes(x=Day, y=Protein.mgcell, fill = Group)) +
geom_boxplot(width=.5, outlier.shape= NA, position = position_dodge(width = 0.5)) +
stat_summary(fun=mean, geom="line", aes(group=Group, color = Group), position = position_dodge(width = 0.5)) +
# stat_summary(fun=mean, geom="point")
geom_point(pch = 21, position=position_jitterdodge(dodge.width=0.5)) +
# ylim(0,0.5) +
scale_fill_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_x_discrete(labels=c("0" = "Day 0", "37" = "Day 37", "52" = "Day 52")) +
xlab("Timepoint") + ylab(expression("Total Protein " (mg~cell^{-1}))) + #Axis titles
theme_bw() + theme(panel.border = element_rect(color="black", fill=NA, size=0.75), panel.grid.major = element_blank(), #Makes background theme white
panel.grid.minor = element_blank(), axis.line = element_blank()) +
theme(axis.text = element_text(size = 15, color = "black"),
axis.title = element_text(size = 18, color = "black")) + ggtitle("Symbiont Total Protein (per cell)")
TP_Sym_Cell_Box
TP_Sym_lmer <- lmer(Symbiont_Protein_mgcm2~Group*Day+(1|Fragment.ID), data = master)
# qqnorm(resid(TP_Sym_lmer))
# qqline(resid(TP_Sym_lmer))
#
# boxplot(resid(TP_Sym_lmer)~master$Group)
# boxplot(resid(TP_Sym_lmer)~master$Day)
anova(TP_Sym_lmer, type="II")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.060275 0.030138 2 12 0.3625 0.7033
## Day 0.171463 0.085732 2 24 1.0312 0.3718
## Group:Day 0.065453 0.016363 4 24 0.1968 0.9376#capture.output(anova(TP_Sym_lmer, type="II"), file = "../output/Statistics/TP_Sym_lmer.csv")
###############################
TP_CS_Box <- ggplot(master, aes(x=Day, y=HostSymRatioProtein, fill = Group)) +
geom_boxplot(width=.5, outlier.shape= NA, position = position_dodge(width = 0.5)) +
stat_summary(fun=mean, geom="line", aes(group=Group, color = Group), position = position_dodge(width = 0.5)) +
# stat_summary(fun=mean, geom="point")
geom_point(pch = 21, position=position_jitterdodge(dodge.width=0.5)) +
# ylim(0,0.5) +
scale_fill_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_x_discrete(labels=c("0" = "Day 0", "37" = "Day 37", "52" = "Day 52")) +
xlab("Timepoint") + ylab("Coral to Symbiont Ratio (Total Protein)") + #Axis titles
theme_bw() + theme(panel.border = element_rect(color="black", fill=NA, size=0.75), panel.grid.major = element_blank(), #Makes background theme white
panel.grid.minor = element_blank(), axis.line = element_blank()) +
theme(axis.text = element_text(size = 15, color = "black"),
axis.title = element_text(size = 18, color = "black")) + ggtitle("Host to Symbiont Ratio of Total Protein")
TP_CS_Box
TP_CS_lmer <- lmer(HostSymRatioProtein~Group*Day+(1|Fragment.ID), data = master)
# qqnorm(resid(TP_CS_lmer))
# qqline(resid(TP_CS_lmer))
#
# boxplot(resid(TP_CS_lmer)~master$Group)
# boxplot(resid(TP_CS_lmer)~master$Day)
anova(TP_CS_lmer, type="II")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.046434 0.023217 2 12 1.4402 0.275047
## Day 0.281598 0.140799 2 24 8.7339 0.001413 **
## Group:Day 0.085401 0.021350 4 24 1.3244 0.289469
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#capture.output(anova(TP_CS_lmer, type="II"), file = "../output/Statistics/TP_CS_lmer.csv")
###############################
Sym_Box <- ggplot(master, aes(x=Day, y=Cells.cm2.x6, fill = Group)) +
geom_boxplot(width=.5, outlier.shape= NA, position = position_dodge(width = 0.5)) +
stat_summary(fun=mean, geom="line", aes(group=Group, color = Group), position = position_dodge(width = 0.5)) +
# stat_summary(fun=mean, geom="point")
geom_point(pch = 21, position=position_jitterdodge(dodge.width=0.5)) +
# ylim(0,0.5) +
scale_fill_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_x_discrete(labels=c("0" = "Day 0", "37" = "Day 37", "52" = "Day 52")) +
xlab("Timepoint") + ylab(expression("Cell Density " (10^{6} ~ cm^{-2})))+ #label y axis + #Axis titles
theme_bw() + theme(panel.border = element_rect(color="black", fill=NA, size=0.75), panel.grid.major = element_blank(), #Makes background theme white
panel.grid.minor = element_blank(), axis.line = element_blank()) +
theme(axis.text = element_text(size = 15, color = "black"),
axis.title = element_text(size = 18, color = "black"))+ ggtitle("Endosymbiont Cell Density (x10^6)")
Sym_Box
Sym_lmer <- lmer(Cells.cm2.x6~Group*Day+(1|Fragment.ID), data = master)
# qqnorm(resid(Sym_lmer))
# qqline(resid(Sym_lmer))
#
# boxplot(resid(Sym_lmer)~master$Group)
# boxplot(resid(Sym_lmer)~master$Day)
anova(Sym_lmer, type="II")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 2.4141 1.2071 2 12 17.927 0.0002486 ***
## Day 10.9398 5.4699 2 24 81.240 2.065e-11 ***
## Group:Day 0.4764 0.1191 4 24 1.769 0.1680518
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#capture.output(anova(Sym_lmer, type="II"), file = "../output/Statistics/Sym_lmer.csv")
#################################
Chla_SA_Box <- ggplot(master, aes(x=Day, y=ChlA.ugcm2, fill = Group)) +
geom_boxplot(width=.5, outlier.shape= NA, position = position_dodge(width = 0.5)) +
stat_summary(fun=mean, geom="line", aes(group=Group, color = Group), position = position_dodge(width = 0.5)) +
# stat_summary(fun=mean, geom="point")
geom_point(pch = 21, position=position_jitterdodge(dodge.width=0.5)) +
# ylim(0,0.5) +
scale_fill_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_x_discrete(labels=c("0" = "Day 0", "37" = "Day 37", "52" = "Day 52")) +
xlab("Timepoint") + ylab(expression("Chlorophyll a " (ug ~ cm^{-2})))+ #label y axis + #Axis titles
theme_bw() + theme(panel.border = element_rect(color="black", fill=NA, size=0.75), panel.grid.major = element_blank(), #Makes background theme white
panel.grid.minor = element_blank(), axis.line = element_blank()) +
theme(axis.text = element_text(size = 15, color = "black"),
axis.title = element_text(size = 18, color = "black")) + ggtitle("Chlorophyll a (per cm2)")
Chla_SA_Box
Chla_SA_lmer <- lmer(ChlA.ugcm2~Group*Day+(1|Fragment.ID), data = master)
# qqnorm(resid(Chla_SA_lmer))
# qqline(resid(Chla_SA_lmer)) # double check normality
#
# boxplot(resid(Chla_SA_lmer)~master$Group)
# boxplot(resid(Chla_SA_lmer)~master$Day)
anova(Chla_SA_lmer, type="II")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 49.211 24.606 2 36 6.5346 0.003792 **
## Day 288.137 144.068 2 36 38.2610 1.234e-09 ***
## Group:Day 2.627 0.657 4 36 0.1744 0.950087
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#capture.output(anova(Chla_SA_lmer, type="II"), file = "../output/Statistics/Chla_SA_lmer.csv")
#################################
ChlC2_SA_Box <- ggplot(master, aes(x=Day, y=ChlC2.ugcm2, fill = Group)) +
geom_boxplot(width=.5, outlier.shape= NA, position = position_dodge(width = 0.5)) +
stat_summary(fun=mean, geom="line", aes(group=Group, color = Group), position = position_dodge(width = 0.5)) +
# stat_summary(fun=mean, geom="point")
geom_point(pch = 21, position=position_jitterdodge(dodge.width=0.5)) +
# ylim(0,0.5) +
scale_fill_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_x_discrete(labels=c("0" = "Day 0", "37" = "Day 37", "52" = "Day 52")) +
xlab("Timepoint") + ylab(expression("Chlorophyll c2 " (ug ~ cm^{-2})))+ #label y axis + #Axis titles
theme_bw() + theme(panel.border = element_rect(color="black", fill=NA, size=0.75), panel.grid.major = element_blank(), #Makes background theme white
panel.grid.minor = element_blank(), axis.line = element_blank()) +
theme(axis.text = element_text(size = 15, color = "black"),
axis.title = element_text(size = 18, color = "black")) + ggtitle("Chlorophyll c2 (per cm2)")
ChlC2_SA_Box
ChlC2_SA_lmer <- lmer(ChlC2.ugcm2~Group*Day+(1|Fragment.ID), data = master)
# qqnorm(resid(ChlC2_SA_lmer))
# qqline(resid(ChlC2_SA_lmer)) # double check normality
#
# boxplot(resid(ChlC2_SA_lmer)~master$Group)
# boxplot(resid(ChlC2_SA_lmer)~master$Day)
anova(ChlC2_SA_lmer, type="II")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 4.3301 2.1650 2 36 1.4932 0.2382414
## Day 31.0503 15.5251 2 36 10.7074 0.0002244 ***
## Group:Day 5.7109 1.4277 4 36 0.9847 0.4281952
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#capture.output(anova(ChlC2_SA_lmer, type="II"), file = "../output/Statistics/ChlC2_SA_lmer.csv")
#################################
Chla_cell_Box <- ggplot(master, aes(x=Day, y=ChlA.ugcell, fill = Group)) +
geom_boxplot(width=.5, outlier.shape= NA, position = position_dodge(width = 0.5)) +
stat_summary(fun=mean, geom="line", aes(group=Group, color = Group), position = position_dodge(width = 0.5)) +
# stat_summary(fun=mean, geom="point")
geom_point(pch = 21, position=position_jitterdodge(dodge.width=0.5)) +
# ylim(0,0.5) +
scale_fill_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_x_discrete(labels=c("0" = "Day 0", "37" = "Day 37", "52" = "Day 52")) +
xlab("Timepoint") + ylab(expression("Chlorophyll a " (ug ~ cell^{-1})))+ #label y axis + #Axis titles
theme_bw() + theme(panel.border = element_rect(color="black", fill=NA, size=0.75), panel.grid.major = element_blank(), #Makes background theme white
panel.grid.minor = element_blank(), axis.line = element_blank()) +
theme(axis.text = element_text(size = 15, color = "black"),
axis.title = element_text(size = 18, color = "black")) + ggtitle("Chlorophyll a (per cell)")
Chla_cell_Box
Chla_cell_lmer <- lmer(ChlA.ugcell~Group*Day+(1|Fragment.ID), data = master)
# qqnorm(resid(Chla_cell_lmer))
# qqline(resid(Chla_cell_lmer)) # double check normality
#
# boxplot(resid(Chla_cell_lmer)~master$Group)
# boxplot(resid(Chla_cell_lmer)~master$Day)
anova(Chla_cell_lmer, type="II")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 1.3966e-11 6.9832e-12 2 1.141 2.8305 0.3611
## Day 5.5439e-12 2.7719e-12 2 1.141 1.1235 0.5375
## Group:Day 1.7957e-11 4.4892e-12 4 1.141 1.8196 0.4774#capture.output(anova(Chla_cell_lmer, type="II"), file = "../output/Statistics/Chla_cell_lmer.csv")
#################################
ChlC2_cell_Box <- ggplot(master, aes(x=Day, y=ChlC2.ugcell, fill = Group)) +
geom_boxplot(width=.5, outlier.shape= NA, position = position_dodge(width = 0.5)) +
stat_summary(fun=mean, geom="line", aes(group=Group, color = Group), position = position_dodge(width = 0.5)) +
# stat_summary(fun=mean, geom="point")
geom_point(pch = 21, position=position_jitterdodge(dodge.width=0.5)) +
# ylim(0,0.5) +
scale_fill_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_x_discrete(labels=c("0" = "Day 0", "37" = "Day 37", "52" = "Day 52")) +
xlab("Timepoint") + ylab(expression("Chlorophyll c2 " (ug ~ cell^{-1})))+ #label y axis + #Axis titles
theme_bw() + theme(panel.border = element_rect(color="black", fill=NA, size=0.75), panel.grid.major = element_blank(), #Makes background theme white
panel.grid.minor = element_blank(), axis.line = element_blank()) +
theme(axis.text = element_text(size = 15, color = "black"),
axis.title = element_text(size = 18, color = "black")) + ggtitle("Chlorophyll C2 (per cell)")
ChlC2_cell_Box
ChlC2_cell_lmer <- lmer(ChlC2.ugcell~Group*Day+(1|Fragment.ID), data = master)
# qqnorm(resid(ChlC2_cell_lmer))
# qqline(resid(ChlC2_cell_lmer)) # double check normality
#
# boxplot(resid(ChlC2_cell_lmer)~master$Group)
# boxplot(resid(ChlC2_cell_lmer)~master$Day)
anova(ChlC2_cell_lmer, type="II")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 6.9132e-12 3.4566e-12 2 1.0236 1.6444 0.4790
## Day 3.2920e-13 1.6460e-13 2 1.0236 0.0783 0.9297
## Group:Day 1.4039e-11 3.5096e-12 4 1.0236 1.6697 0.5139#capture.output(anova(ChlC2_cell_lmer, type="II"), file = "../output/Statistics/ChlC2_cell_lmer.csv")
#################################
Carb_Coral_Box <- ggplot(master, aes(x=Day, y=Coral_Carb_mgcm2, fill = Group)) +
geom_boxplot(width=.5, outlier.shape= NA, position = position_dodge(width = 0.5)) +
stat_summary(fun=mean, geom="line", aes(group=Group, color = Group), position = position_dodge(width = 0.5)) +
# stat_summary(fun=mean, geom="point")
geom_point(pch = 21, position=position_jitterdodge(dodge.width=0.5)) +
# ylim(0,0.5) +
scale_fill_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_x_discrete(labels=c("0" = "Day 0", "37" = "Day 37", "52" = "Day 52")) +
xlab("Timepoint") + ylab(expression("Total Carbohydrate " (mg~cm^{-2}))) + #Axis titles
theme_bw() + theme(panel.border = element_rect(color="black", fill=NA, size=0.75), panel.grid.major = element_blank(), #Makes background theme white
panel.grid.minor = element_blank(), axis.line = element_blank()) +
theme(axis.text = element_text(size = 15, color = "black"),
axis.title = element_text(size = 18, color = "black")) + ggtitle("Coral Total Carbohydrate (per cm2)")
Carb_Coral_Box
Carb_Coral_lmer <- lmer(Coral_Carb_mgcm2~Group*Day+(1|Fragment.ID), data = master)
# qqnorm(resid(Carb_Coral_lmer))
# qqline(resid(Carb_Coral_lmer)) # double check normality
#
# boxplot(resid(Carb_Coral_lmer)~master$Group)
# boxplot(resid(Carb_Coral_lmer)~master$Day)
anova(Carb_Coral_lmer, type="II")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 19.129 9.565 2 11.992 2.1777 0.156027
## Day 71.143 35.572 2 23.992 8.0990 0.002053 **
## Group:Day 20.127 5.032 4 23.992 1.1456 0.359181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#capture.output(anova(Carb_Coral_lmer, type="II"), file = "../output/Statistics/Carb_Coral_lmer.csv")
#################################
Carb_Sym_SA_Box <- ggplot(master, aes(x=Day, y=Symbiont_Carb_mgcm2, fill = Group)) +
geom_boxplot(width=.5, outlier.shape= NA, position = position_dodge(width = 0.5)) +
stat_summary(fun=mean, geom="line", aes(group=Group, color = Group), position = position_dodge(width = 0.5)) +
# stat_summary(fun=mean, geom="point")
geom_point(pch = 21, position=position_jitterdodge(dodge.width=0.5)) +
# ylim(0,0.5) +
scale_fill_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_x_discrete(labels=c("0" = "Day 0", "37" = "Day 37", "52" = "Day 52")) +
xlab("Timepoint") + ylab(expression("Total Carbohydrate " (mg~cm^{-2}))) + #Axis titles
theme_bw() + theme(panel.border = element_rect(color="black", fill=NA, size=0.75), panel.grid.major = element_blank(), #Makes background theme white
panel.grid.minor = element_blank(), axis.line = element_blank()) +
theme(axis.text = element_text(size = 15, color = "black"),
axis.title = element_text(size = 18, color = "black")) + ggtitle("Symbiont Total Carbohydrate (per cm2)")
Carb_Sym_SA_Box
Carb_Sym_SA_lmer <- lmer(Symbiont_Carb_mgcm2~Group*Day+(1|Fragment.ID), data = master)
# qqnorm(resid(Carb_Sym_SA_lmer))
# qqline(resid(Carb_Sym_SA_lmer)) # double check normality
#
# boxplot(resid(Carb_Sym_SA_lmer)~master$Group)
# boxplot(resid(Carb_Sym_SA_lmer)~master$Day)
anova(Carb_Sym_SA_lmer, type="II")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.30567 0.15283 2 12 0.1354 0.8747
## Day 1.87116 0.93558 2 24 0.8290 0.4486
## Group:Day 1.39687 0.34922 4 24 0.3094 0.8688#capture.output(anova(Carb_Sym_SA_lmer, type="II"), file = "../output/Statistics/Carb_Sym_SA_lmer.csv")
#################################
Carb_Sym_Cell_Box <- ggplot(master, aes(x=Day, y=Carb.mgcell, fill = Group)) +
geom_boxplot(width=.5, outlier.shape= NA, position = position_dodge(width = 0.5)) +
stat_summary(fun=mean, geom="line", aes(group=Group, color = Group), position = position_dodge(width = 0.5)) +
# stat_summary(fun=mean, geom="point")
geom_point(pch = 21, position=position_jitterdodge(dodge.width=0.5)) +
# ylim(0,0.5) +
scale_fill_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_x_discrete(labels=c("0" = "Day 0", "37" = "Day 37", "52" = "Day 52")) +
xlab("Timepoint") + ylab(expression("Total Carbohydrate " (mg~cm^{-2}))) + #Axis titles
theme_bw() + theme(panel.border = element_rect(color="black", fill=NA, size=0.75), panel.grid.major = element_blank(), #Makes background theme white
panel.grid.minor = element_blank(), axis.line = element_blank()) +
theme(axis.text = element_text(size = 15, color = "black"),
axis.title = element_text(size = 18, color = "black")) + ggtitle("Symbiont Total Carbohydrate (per cell)")
Carb_Sym_Cell_Box
Carb_Sym_Cell_lmer <- lmer(Carb.mgcell~Group*Day+(1|Fragment.ID), data = master)
# qqnorm(resid(Carb_Sym_Cell_lmer))
# qqline(resid(Carb_Sym_Cell_lmer)) # double check normality
#
# boxplot(resid(Carb_Sym_Cell_lmer)~master$Group)
# boxplot(resid(Carb_Sym_Cell_lmer)~master$Day)
anova(Carb_Sym_Cell_lmer, type="II")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 3.2244e-10 1.6122e-10 2 2.2679 12.7207 0.05853 .
## Day 4.4263e-10 2.2131e-10 2 2.7318 17.4623 0.02778 *
## Group:Day 1.5965e-10 3.9911e-11 4 2.7318 3.1491 0.20124
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#capture.output(anova(Carb_Sym_Cell_lmer, type="II"), file = "../output/Statistics/Carb_Sym_Cell_lmer.csv")
#################################
Carb_CS_Box <- ggplot(master, aes(x=Day, y=HostSymRatioCarb, fill = Group)) +
geom_boxplot(width=.5, outlier.shape= NA, position = position_dodge(width = 0.5)) +
stat_summary(fun=mean, geom="line", aes(group=Group, color = Group), position = position_dodge(width = 0.5)) +
# stat_summary(fun=mean, geom="point")
geom_point(pch = 21, position=position_jitterdodge(dodge.width=0.5)) +
# ylim(0,0.5) +
scale_fill_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_x_discrete(labels=c("0" = "Day 0", "37" = "Day 37", "52" = "Day 52")) +
xlab("Timepoint") + ylab("Coral to Symbiont Ratio (Total Carbohydrate)") + #Axis titles
theme_bw() + theme(panel.border = element_rect(color="black", fill=NA, size=0.75), panel.grid.major = element_blank(), #Makes background theme white
panel.grid.minor = element_blank(), axis.line = element_blank()) +
theme(axis.text = element_text(size = 15, color = "black"),
axis.title = element_text(size = 18, color = "black")) + ggtitle("Host to Symbiont Total Carbohydrate")
Carb_CS_Box
Carb_CS_lmer <- lmer(HostSymRatioCarb~Group*Day+(1|Fragment.ID), data = master)
# qqnorm(resid(Carb_CS_lmer))
# qqline(resid(Carb_CS_lmer)) # double check normality
#
# boxplot(resid(Carb_CS_lmer)~master$Group)
# boxplot(resid(Carb_CS_lmer)~master$Day)
anova(Carb_CS_lmer, type="II")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 12.947 6.4738 2 12 1.3349 0.2996
## Day 10.740 5.3701 2 24 1.1073 0.3468
## Group:Day 10.730 2.6824 4 24 0.5531 0.6987#capture.output(anova(Carb_CS_lmer, type="II"), file = "../output/Statistics/Carb_CS_lmer.csv")
#################################
PG_Box <- ggplot(master, aes(x=Day, y=Pgross_umol.cm2.hr, fill = Group)) +
geom_boxplot(width=.5, outlier.shape= NA, position = position_dodge(width = 0.5)) +
stat_summary(fun=mean, geom="line", aes(group=Group, color = Group), position = position_dodge(width = 0.5)) +
# stat_summary(fun=mean, geom="point")
geom_point(pch = 21, position=position_jitterdodge(dodge.width=0.5), outlier.shape= NA) +
# ylim(0,0.5) +
scale_fill_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_x_discrete(labels=c("T1" = "Day 0", "T2" = "Day 37", "T3" = "Day 52")) +
xlab("Timepoint") + ylab(expression("Gross Photosynthetic Rate " (mu*mol ~ cm^{-2} ~ h^{-1}))) + #label y axis + #Axis titles
theme_bw() + theme(panel.border = element_rect(color="black", fill=NA, size=0.75), panel.grid.major = element_blank(), #Makes background theme white
panel.grid.minor = element_blank(), axis.line = element_blank()) +
theme(axis.text = element_text(size = 15, color = "black"),
axis.title = element_text(size = 18, color = "black")) + ggtitle("Gross Photosynthetic Rate")
PG_Box
PG_lmer <- lmer(Pgross_umol.cm2.hr~Group*Day+(1|Fragment.ID), data = master)
# qqnorm(resid(PG_lmer))
# qqline(resid(PG_lmer)) # double check normality
#
# boxplot(resid(PG_lmer)~master$Group)
# boxplot(resid(PG_lmer)~master$Day)
anova(PG_lmer, type="II")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.6182 0.30910 2 12 14.942 0.000553 ***
## Day 3.2558 1.62790 2 24 78.696 2.878e-11 ***
## Group:Day 1.3409 0.33522 4 24 16.205 1.478e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#capture.output(anova(PG_lmer, type="II"), file = "../output/Statistics/PG_lmer.csv")
#################################
Resp_Box <- ggplot(master, aes(x=Day, y=abs.Rdark_umol.cm2.hr, fill = Group)) +
geom_boxplot(width=.5, outlier.shape= NA, position = position_dodge(width = 0.5)) +
stat_summary(fun=mean, geom="line", aes(group=Group, color = Group), position = position_dodge(width = 0.5)) +
# stat_summary(fun=mean, geom="point")
geom_point(pch = 21, position=position_jitterdodge(dodge.width=0.5), outlier.shape= NA) +
# ylim(0,0.5) +
scale_fill_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_x_discrete(labels=c("T1" = "Day 0", "T2" = "Day 37", "T3" = "Day 52")) +
xlab("Timepoint") + ylab(expression("Respiration Rate " (mu*mol ~ cm^{-2} ~ h^{-1}))) + #label y axis + #Axis titles
theme_bw() + theme(panel.border = element_rect(color="black", fill=NA, size=0.75), panel.grid.major = element_blank(), #Makes background theme white
panel.grid.minor = element_blank(), axis.line = element_blank()) +
theme(axis.text = element_text(size = 15, color = "black"),
axis.title = element_text(size = 18, color = "black")) + ggtitle("Light-Enhanced Respiration Rate")
Resp_Box
Resp_lmer <- lmer(abs.Rdark_umol.cm2.hr~Group*Day+(1|Fragment.ID), data = master)
# qqnorm(resid(Resp_lmer))
# qqline(resid(Resp_lmer)) # double check normality
#
# boxplot(resid(Resp_lmer)~master$Group)
# boxplot(resid(Resp_lmer)~master$Day)
anova(Resp_lmer, type="II")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.04322 0.021610 2 36 1.9296 0.1599
## Day 0.57080 0.285398 2 36 25.4839 1.273e-07 ***
## Group:Day 0.03669 0.009172 4 36 0.8190 0.5216
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#capture.output(anova(Resp_lmer, type="II"), file = "../output/Statistics/Resp_lmer.csv")
#################################
PR_Box <- ggplot(master, aes(x=Day, y=PR, fill = Group)) +
geom_boxplot(width=.5, outlier.shape= NA, position = position_dodge(width = 0.5)) +
stat_summary(fun=mean, geom="line", aes(group=Group, color = Group), position = position_dodge(width = 0.5)) +
# stat_summary(fun=mean, geom="point")
geom_point(pch = 21, position=position_jitterdodge(dodge.width=0.5), outlier.shape= NA) +
# ylim(0,0.5) +
scale_fill_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#E7B800", "#00AFBB", "#FC4E07")) +
scale_x_discrete(labels=c("T1" = "Day 0", "T2" = "Day 37", "T3" = "Day 52")) +
xlab("Timepoint") + ylab("P:R") + #label y axis + #Axis titles
theme_bw() + theme(panel.border = element_rect(color="black", fill=NA, size=0.75), panel.grid.major = element_blank(), #Makes background theme white
panel.grid.minor = element_blank(), axis.line = element_blank()) +
theme(axis.text = element_text(size = 15, color = "black"),
axis.title = element_text(size = 18, color = "black")) + ggtitle("Photosyntheis to Respiration Ratio")
PR_Box
PR_lmer <- lmer(PR~Group*Day+(1|Fragment.ID), data = master)
# qqnorm(resid(PR_lmer))
# qqline(resid(PR_lmer)) # double check normality
#
# boxplot(resid(PR_lmer)~master$Group)
# boxplot(resid(PR_lmer)~master$Day)
anova(PR_lmer, type="II")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 4.3386 2.1693 2 12 41.894 3.866e-06 ***
## Day 4.2652 2.1326 2 24 41.185 1.741e-08 ***
## Group:Day 4.3340 1.0835 4 24 20.924 1.549e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#capture.output(anova(PR_lmer, type="II"), file = "../output/Statistics/PR_lmer.csv")
#################################PCA
master.pca <- master %>%
dplyr::select(-(Surface_Area_cm2))
scaled_pca <-prcomp(master.pca[c(4:20)], scale=TRUE, center=TRUE)
fviz_eig(scaled_pca)
coral_info<-master.pca[c(2,3)]
pca_data<- scaled_pca%>%
augment(coral_info)%>%
group_by(Day, Group)%>%
mutate(PC1.mean = mean(.fittedPC1),
PC2.mean = mean(.fittedPC2))
pca.centroids<- pca_data%>%
dplyr::select(Day, Group, PC1.mean, PC2.mean)%>%
dplyr::group_by(Day, Group)%>%
dplyr::summarise(PC1.mean = mean(PC1.mean),
PC2.mean = mean(PC2.mean))
#Examine PERMANOVA results.
# scale data
vegan <- scale(master.pca[c(4:20)])
# PerMANOVA
permanova<-adonis(vegan ~ Day*Group, data = master.pca, method='eu')
z_pca<-permanova$aov.tab
z_pca## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Day 2 200.80 100.400 9.4360 0.26845 0.001 ***
## Group 2 98.64 49.322 4.6354 0.13188 0.001 ***
## Day:Group 4 65.51 16.377 1.5392 0.08758 0.074 .
## Residuals 36 383.05 10.640 0.51209
## Total 44 748.00 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Assemble plot with background points
#1. make plot with dots
#adding percentages on axis
names(pca_data)[4] <- "PCA1"
names(pca_data)[5] <- "PCA2"
percentage <- round((scaled_pca$sdev^2) / sum((scaled_pca$sdev^2)) * 100, 2)
percentage <- paste( colnames(pca_data[4:20]), "(", paste(as.character(percentage), "%", ")", sep="") )
#setting up data to add polygons
pca_data$Day.Group <- paste(pca_data$Day, pca_data$Group)
find_hull <- function(pca_data) pca_data[chull(pca_data$PCA1, pca_data$PCA2), ]
hulls <- ddply(pca_data, "Day.Group", find_hull)
PCA<-ggplot(pca_data, aes(PCA1, PCA2, color=Group)) +
geom_point(size = 4, alpha=0.2, aes(shape = Day))+
scale_colour_manual(values=c("#46008B", "#8B0046", "#468B00")) +
scale_fill_manual(values=c("#46008B", "#8B0046", "#468B00")) +
scale_shape_manual(values=c(15, 17, 19)) +
theme_classic()+
ylim(-7,12)+
xlim(-6,6)+
ylab(percentage[2])+
xlab(percentage[1])+
geom_text(x=4.5, y=-4.75, label=paste("p(Day)=", z_pca$`Pr(>F)`[1]), size=4, color=ifelse(z_pca$`Pr(>F)`[1] < 0.05, "black", "darkgray")) +
geom_text(x=4.5, y=-5.5, label=paste("p(Group)=", z_pca$`Pr(>F)`[2]), size=4, color=ifelse(z_pca$`Pr(>F)`[2] < 0.05, "black", "darkgray")) +
geom_text(x=4.5, y=-6.25, label=paste("p(Day x Group)=", z_pca$`Pr(>F)`[3]), size=4, color=ifelse(z_pca$`Pr(>F)`[3] < 0.05, "black", "darkgray")) +
theme(legend.text = element_text(size=8),
legend.position="none",
plot.background = element_blank(),
legend.title = element_text(size=10),
plot.margin = margin(1, 1, 1, 1, "cm"),
axis.text = element_text(size=18),
title = element_text(size=25, face="bold"),
axis.title = element_text(size=18))
#Add centroids
#2. add centroids
PCAcen<-PCA + geom_polygon(data = hulls, alpha = 0.2, aes(color = Group, fill = Group, lty = Day)) +
geom_point(aes(x=PC1.mean, y=PC2.mean,color=Group, shape = Day), data=pca.centroids, size=4, show.legend=FALSE) +
scale_linetype_manual(values = c("solid", "dashed", "dotted")) +
scale_colour_manual(values=c("#46008B", "#8B0046", "#468B00"), breaks = c("Control","Bleached", "Mortality"), labels = c("Control", "Bleached", "Partial Mortality")) +
scale_fill_manual(values=c("#46008B", "#8B0046", "#468B00"), breaks = c("Control","Bleached", "Mortality"), labels = c("Control", "Bleached", "Partial Mortality")) +
scale_shape_manual(values=c(15, 17, 19)) +
theme(legend.text = element_text(size=8),
legend.position=c(0.95,0.85),
plot.background = element_blank(),
legend.title = element_text(size=10),
plot.margin = margin(1, 1, 1, 1, "cm"),
axis.text = element_text(size=18),
title = element_text(size=25, face="bold"),
axis.title = element_text(size=18))
#Add segments
#3. add segments
segpoints<-pca.centroids%>%
gather(variable, value, -(Day:Group)) %>%
unite(temp, Day, variable) %>%
spread(temp, value)
names(segpoints)[2] <- "Day0_PC1.mean"
names(segpoints)[3] <- "Day0_PC2.mean"
names(segpoints)[4] <- "Day37_PC1.mean"
names(segpoints)[5] <- "Day37_PC2.mean"
names(segpoints)[6] <- "Day52_PC1.mean"
names(segpoints)[7] <- "Day52_PC2.mean"
PCAfull<-PCAcen +
geom_segment(aes(x = Day0_PC1.mean, y = Day0_PC2.mean, xend = Day37_PC1.mean, yend = Day37_PC2.mean, colour = Group), data = segpoints, size=2, show.legend=FALSE) +
geom_segment(aes(x = Day37_PC1.mean, y = Day37_PC2.mean, xend = Day52_PC1.mean, yend = Day52_PC2.mean, colour = Group), data = segpoints, size=2, arrow = arrow(length=unit(0.5,"cm")), show.legend=FALSE); PCAfull
#Add bi plot with loadings
#1. make plot with dots
biplotArrows<-ggplot2::autoplot(scaled_pca, data=master.pca, loadings=TRUE, colour="Group", loadings.label.colour="black", loadings.colour="black", loadings.label=TRUE, frame=FALSE, loadings.label.size=3.5, loadings.label.vjust=0.5, loadings.label.hjust=-0.1, loadings.label.repel=FALSE, size=4, alpha=0.0) +
#scale_colour_manual(values=c("blue4", "darkgray", "springgreen4")) +
#scale_fill_manual(values=c("blue4", "darkgray", "springgreen4")) +
theme_classic()+
#xlim(-.3,.6)+
#ylim(-.3, .3)+
theme(legend.text = element_text(size=18),
legend.position="none",
axis.text.x=element_blank(),
axis.text.y=element_blank(),
plot.background = element_blank(),
legend.title = element_blank(),
plot.margin = margin(1, 1, 1, 1, "cm"),
axis.text = element_text(size=18),
title = element_text(size=25, face="bold"),
axis.title = element_blank());biplotArrows
FinalFullPCA<-ggdraw(PCAfull) + #theme_half_open(12)) +
draw_plot(biplotArrows, .05, .6, .4, .4); FinalFullPCA #x, y, w, h
ggsave(filename="../output/Physiology/FullPCA_phys.pdf", plot=FinalFullPCA, dpi=300, width=12, height=10, units="in")